Adjustable autonomy: a systematic literature review

Mostafa, Salama A.; Ahmad, Mohd Sharifuddin; Mustapha, Aida

doi:10.1007/s10462-017-9560-8

Cited by 59 publications

(28 citation statements)

References 56 publications

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“…Research on AI autonomy is diverse and involves, for example, the autonomy of robots (Noorman and Johnson 2014), human-robot interactions (Goodrich and Schultz 2007), or the coordination of several autonomous agents (Yan et al 2013). Of particular concern in relation to this principle is research on trust in autonomous systems such as autonomous vehicles (Schaefer et al 2016;Stormont 2008), as well as research on adjustable autonomy, which refers to agents dynamically changing their autonomy and transferring it to other entities (Mostafa et al 2019). For organizations, this principle implies that they should, for example, consider implementing proper oversight mechanisms (e.g., keeping the human-in-the-loop) to ensure autonomy when embedding AI into their electronic services and products.…”

Section: Autonomymentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

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Section: Autonomymentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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“…[104]. This method can be used to model human behaviors and their effects on others [105]. Social scientists have begun to convert social theories to computer programs [106].…”

Section: B Development Of a Configuration Framework To Generate An Ementioning

confidence: 99%

A Systematic Review on Students’ Engagement in Classroom: Indicators, Challenges and Computational Techniques

Subramainan¹,

Mahmoud²

2020

IJACSA

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Students' engagement in a classroom is a key factor that influences several educational outcomes. Studies by the University of California, Los Angeles (UCLA) and British universities found that 40% of students are frequently experiencing boredom and less than 20% of students ask questions in class due to poor engagement. A survey by Malaysia's Program for International Student Assessment (PISA) found that 80% of the participating schools fell into the poor performance bracket. However, studies in this line of research are limited and scattered. To provide a clear insight into this problem and support researchers, it is crucial to understand the current state of research in this area. Consequently, in this paper, a comprehensive review is conducted to map the literature studies to a consistent taxonomy. Search terms revealed 87 papers from several databases that have been classified into seven categories. A systematic review method is applied, analysis is performed, and finally, findings, discussion, and recommendations are presented.

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“…whereas an extensive survey is presented by Mostafa et al (2019). Bruemmer et al (2005), Leeper et al (2012), Chen et al (2013), and Muszynski et al (2012) propose teleoperation systems with different LOA for control of ground-based robots from classical egocentric and exocentric views.…”

Section: Levels Of Automation and Approaches For Controlmentioning

confidence: 99%

High-Level Teleoperation System for Aerial Exploration of Indoor Environments

et al. 2019

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Exploration of challenging indoor environments is a demanding task. While automation with aerial robots seems a promising solution, fully autonomous systems still struggle with high-level cognitive tasks and intuitive decision making. To facilitate automation, we introduce a novel teleoperation system with an aerial telerobot that is capable of handling all demanding low-level tasks. Motivated by the typical structure of indoor environments, the system creates an interactive scene topology in real-time that reduces scene details and supports affordances. Thus, difficult high-level tasks can be effectively supervised by a human operator. To elaborate on the effectiveness of our system during a real-world exploration mission, we conducted a user study. Despite being limited by real-world constraints, results indicate that our system better supports operators with indoor exploration, compared to a baseline system with traditional joystick control.

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Adjustable autonomy: a systematic literature review

Cited by 59 publications

References 56 publications

Trustworthy artificial intelligence

Trustworthy artificial intelligence

A Systematic Review on Students’ Engagement in Classroom: Indicators, Challenges and Computational Techniques

High-Level Teleoperation System for Aerial Exploration of Indoor Environments

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